Overview

Brought to you by YData

Dataset statistics

Number of variables31
Number of observations14036
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.7 MiB
Average record size in memory428.2 B

Variable types

Numeric24
Categorical6
Text1

Timeseries statistics

Number of series0
Time series length14036
Starting point0
Ending point14035
Period1
2025-04-18T10:53:06.114888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:53:06.160248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Alerts

authentication_status is highly overall correlated with connection_count and 1 other fieldsHigh correlation
class is highly overall correlated with connection_status and 5 other fieldsHigh correlation
connection_count is highly overall correlated with authentication_status and 1 other fieldsHigh correlation
connection_status is highly overall correlated with classHigh correlation
destination_bytes is highly overall correlated with destination_different_server_rate and 2 other fieldsHigh correlation
destination_different_server_rate is highly overall correlated with destination_bytes and 2 other fieldsHigh correlation
destination_host_count is highly overall correlated with destination_same_source_port_rate and 1 other fieldsHigh correlation
destination_host_server_count is highly overall correlated with destination_bytes and 2 other fieldsHigh correlation
destination_reset_error_rate is highly overall correlated with destination_server_reset_error_rate and 2 other fieldsHigh correlation
destination_same_server_rate is highly overall correlated with destination_bytes and 2 other fieldsHigh correlation
destination_same_source_port_rate is highly overall correlated with destination_host_countHigh correlation
destination_server_different_host_rate is highly overall correlated with destination_host_countHigh correlation
destination_server_reset_error_rate is highly overall correlated with destination_reset_error_rate and 2 other fieldsHigh correlation
destination_server_syn_error_rate is highly overall correlated with class and 2 other fieldsHigh correlation
destination_syn_error_rate is highly overall correlated with class and 3 other fieldsHigh correlation
different_server_rate is highly overall correlated with same_server_rateHigh correlation
guest_login is highly overall correlated with suspicious_activityHigh correlation
protocol is highly overall correlated with authentication_status and 1 other fieldsHigh correlation
reset_error_rate is highly overall correlated with destination_reset_error_rate and 2 other fieldsHigh correlation
same_server_rate is highly overall correlated with class and 1 other fieldsHigh correlation
server_request_count is highly overall correlated with connection_count and 1 other fieldsHigh correlation
server_reset_error_rate is highly overall correlated with destination_reset_error_rate and 2 other fieldsHigh correlation
server_syn_error_rate is highly overall correlated with class and 3 other fieldsHigh correlation
suspicious_activity is highly overall correlated with guest_loginHigh correlation
syn_error_rate is highly overall correlated with class and 2 other fieldsHigh correlation
connection_status is highly imbalanced (81.9%) Imbalance
fragment_errors is highly imbalanced (99.5%) Imbalance
guest_login is highly imbalanced (90.5%) Imbalance
class is highly imbalanced (74.9%) Imbalance
compromised_count is highly skewed (γ1 = 46.45181051) Skewed
source_bytes is highly skewed (γ1 = 23.3660419) Skewed
destination_bytes is highly skewed (γ1 = 63.61840747) Skewed
syn_error_rate has 13197 (94.0%) zeros Zeros
connection_time has 12418 (88.5%) zeros Zeros
destination_same_source_port_rate has 4908 (35.0%) zeros Zeros
destination_different_server_rate has 8020 (57.1%) zeros Zeros
destination_server_different_host_rate has 7285 (51.9%) zeros Zeros
suspicious_activity has 13783 (98.2%) zeros Zeros
server_different_host_rate has 9121 (65.0%) zeros Zeros
destination_server_syn_error_rate has 12549 (89.4%) zeros Zeros
destination_same_server_rate has 286 (2.0%) zeros Zeros
compromised_count has 13931 (99.3%) zeros Zeros
source_bytes has 1134 (8.1%) zeros Zeros
destination_syn_error_rate has 12408 (88.4%) zeros Zeros
reset_error_rate has 13294 (94.7%) zeros Zeros
destination_server_reset_error_rate has 12691 (90.4%) zeros Zeros
server_reset_error_rate has 13215 (94.2%) zeros Zeros
server_syn_error_rate has 13194 (94.0%) zeros Zeros
destination_bytes has 2728 (19.4%) zeros Zeros
destination_reset_error_rate has 12461 (88.8%) zeros Zeros
different_server_rate has 12652 (90.1%) zeros Zeros

Reproduction

Analysis started2025-04-18 08:52:27.250077
Analysis finished2025-04-18 08:53:06.101806
Duration38.85 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

syn_error_rate
Real number (ℝ)

High correlation  Zeros 

Distinct34
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.039788401
Minimum0
Maximum1
Zeros13197
Zeros (%)94.0%
Negative0
Negative (%)0.0%
Memory size109.8 KiB
2025-04-18T10:53:06.215140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.17
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.18339176
Coefficient of variation (CV)4.6091764
Kurtosis21.607989
Mean0.039788401
Median Absolute Deviation (MAD)0
Skewness4.7810569
Sum558.47
Variance0.033632538
MonotonicityNot monotonic
2025-04-18T10:53:06.272264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 13197
94.0%
1 437
 
3.1%
0.5 117
 
0.8%
0.33 47
 
0.3%
0.25 40
 
0.3%
0.05 29
 
0.2%
0.2 26
 
0.2%
0.17 20
 
0.1%
0.06 18
 
0.1%
0.07 14
 
0.1%
Other values (24) 91
 
0.6%
ValueCountFrequency (%)
0 13197
94.0%
0.01 1
 
< 0.1%
0.02 4
 
< 0.1%
0.03 8
 
0.1%
0.04 13
 
0.1%
0.05 29
 
0.2%
0.06 18
 
0.1%
0.07 14
 
0.1%
0.08 10
 
0.1%
0.09 8
 
0.1%
ValueCountFrequency (%)
1 437
3.1%
0.99 9
 
0.1%
0.98 2
 
< 0.1%
0.97 1
 
< 0.1%
0.96 1
 
< 0.1%
0.95 1
 
< 0.1%
0.9 2
 
< 0.1%
0.78 1
 
< 0.1%
0.77 1
 
< 0.1%
0.75 2
 
< 0.1%

connection_time
Real number (ℝ)

Zeros 

Distinct526
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean184.19129
Minimum0
Maximum41476
Zeros12418
Zeros (%)88.5%
Negative0
Negative (%)0.0%
Memory size109.8 KiB
2025-04-18T10:53:06.332067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile23
Maximum41476
Range41476
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1448.6041
Coefficient of variation (CV)7.8646718
Kurtosis202.93672
Mean184.19129
Median Absolute Deviation (MAD)0
Skewness12.143431
Sum2585309
Variance2098453.8
MonotonicityNot monotonic
2025-04-18T10:53:06.394815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12418
88.5%
1 316
 
2.3%
2 141
 
1.0%
3 86
 
0.6%
4 58
 
0.4%
5 57
 
0.4%
28 38
 
0.3%
27 38
 
0.3%
6 32
 
0.2%
7 23
 
0.2%
Other values (516) 829
 
5.9%
ValueCountFrequency (%)
0 12418
88.5%
1 316
 
2.3%
2 141
 
1.0%
3 86
 
0.6%
4 58
 
0.4%
5 57
 
0.4%
6 32
 
0.2%
7 23
 
0.2%
8 14
 
0.1%
9 19
 
0.1%
ValueCountFrequency (%)
41476 1
< 0.1%
38818 1
< 0.1%
37072 1
< 0.1%
29053 1
< 0.1%
26525 1
< 0.1%
26090 1
< 0.1%
25420 1
< 0.1%
23120 1
< 0.1%
20252 2
< 0.1%
19759 1
< 0.1%

destination_same_source_port_rate
Real number (ℝ)

High correlation  Zeros 

Distinct101
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.12231476
Minimum0
Maximum1
Zeros4908
Zeros (%)35.0%
Negative0
Negative (%)0.0%
Memory size109.8 KiB
2025-04-18T10:53:06.456810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.01
Q30.08
95-th percentile0.94
Maximum1
Range1
Interquartile range (IQR)0.08

Descriptive statistics

Standard deviation0.25743254
Coefficient of variation (CV)2.1046727
Kurtosis5.2379736
Mean0.12231476
Median Absolute Deviation (MAD)0.01
Skewness2.5219937
Sum1716.81
Variance0.066271514
MonotonicityNot monotonic
2025-04-18T10:53:06.522071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4908
35.0%
0.01 3040
21.7%
0.02 942
 
6.7%
0.03 564
 
4.0%
1 545
 
3.9%
0.04 402
 
2.9%
0.05 281
 
2.0%
0.08 208
 
1.5%
0.5 205
 
1.5%
0.06 198
 
1.4%
Other values (91) 2743
19.5%
ValueCountFrequency (%)
0 4908
35.0%
0.01 3040
21.7%
0.02 942
 
6.7%
0.03 564
 
4.0%
0.04 402
 
2.9%
0.05 281
 
2.0%
0.06 198
 
1.4%
0.07 173
 
1.2%
0.08 208
 
1.5%
0.09 128
 
0.9%
ValueCountFrequency (%)
1 545
3.9%
0.99 18
 
0.1%
0.98 34
 
0.2%
0.97 25
 
0.2%
0.96 31
 
0.2%
0.95 47
 
0.3%
0.94 11
 
0.1%
0.93 27
 
0.2%
0.92 11
 
0.1%
0.91 24
 
0.2%

destination_different_server_rate
Real number (ℝ)

High correlation  Zeros 

Distinct97
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.044255486
Minimum0
Maximum1
Zeros8020
Zeros (%)57.1%
Negative0
Negative (%)0.0%
Memory size109.8 KiB
2025-04-18T10:53:06.584160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.02
95-th percentile0.23
Maximum1
Range1
Interquartile range (IQR)0.02

Descriptive statistics

Standard deviation0.13523304
Coefficient of variation (CV)3.055735
Kurtosis20.800039
Mean0.044255486
Median Absolute Deviation (MAD)0
Skewness4.4891412
Sum621.17
Variance0.018287974
MonotonicityNot monotonic
2025-04-18T10:53:06.648909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8020
57.1%
0.01 1740
 
12.4%
0.02 879
 
6.3%
0.03 634
 
4.5%
0.04 389
 
2.8%
0.07 355
 
2.5%
0.05 322
 
2.3%
0.06 296
 
2.1%
0.08 186
 
1.3%
0.09 127
 
0.9%
Other values (87) 1088
 
7.8%
ValueCountFrequency (%)
0 8020
57.1%
0.01 1740
 
12.4%
0.02 879
 
6.3%
0.03 634
 
4.5%
0.04 389
 
2.8%
0.05 322
 
2.3%
0.06 296
 
2.1%
0.07 355
 
2.5%
0.08 186
 
1.3%
0.09 127
 
0.9%
ValueCountFrequency (%)
1 32
0.2%
0.98 1
 
< 0.1%
0.97 3
 
< 0.1%
0.96 1
 
< 0.1%
0.95 2
 
< 0.1%
0.94 1
 
< 0.1%
0.9 1
 
< 0.1%
0.89 1
 
< 0.1%
0.88 1
 
< 0.1%
0.87 6
 
< 0.1%

connection_status
Categorical

High correlation  Imbalance 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size700.0 KiB
SF
12781 
REJ
 
591
S0
 
439
S1
 
86
RSTO
 
58
Other values (5)
 
81

Length

Max length6
Median length2
Mean length2.0574238
Min length2

Characters and Unicode

Total characters28878
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowSF
2nd rowS0
3rd rowS0
4th rowS0
5th rowS0

Common Values

ValueCountFrequency (%)
SF 12781
91.1%
REJ 591
 
4.2%
S0 439
 
3.1%
S1 86
 
0.6%
RSTO 58
 
0.4%
RSTR 47
 
0.3%
S2 17
 
0.1%
S3 15
 
0.1%
RSTOS0 1
 
< 0.1%
OTH 1
 
< 0.1%

Length

2025-04-18T10:53:06.712775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-18T10:53:06.763749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
sf 12781
91.1%
rej 591
 
4.2%
s0 439
 
3.1%
s1 86
 
0.6%
rsto 58
 
0.4%
rstr 47
 
0.3%
s2 17
 
0.1%
s3 15
 
0.1%
rstos0 1
 
< 0.1%
oth 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
S 13445
46.6%
F 12781
44.3%
R 744
 
2.6%
E 591
 
2.0%
J 591
 
2.0%
0 440
 
1.5%
T 107
 
0.4%
1 86
 
0.3%
O 60
 
0.2%
2 17
 
0.1%
Other values (2) 16
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 28878
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 13445
46.6%
F 12781
44.3%
R 744
 
2.6%
E 591
 
2.0%
J 591
 
2.0%
0 440
 
1.5%
T 107
 
0.4%
1 86
 
0.3%
O 60
 
0.2%
2 17
 
0.1%
Other values (2) 16
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 28878
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 13445
46.6%
F 12781
44.3%
R 744
 
2.6%
E 591
 
2.0%
J 591
 
2.0%
0 440
 
1.5%
T 107
 
0.4%
1 86
 
0.3%
O 60
 
0.2%
2 17
 
0.1%
Other values (2) 16
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 28878
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 13445
46.6%
F 12781
44.3%
R 744
 
2.6%
E 591
 
2.0%
J 591
 
2.0%
0 440
 
1.5%
T 107
 
0.4%
1 86
 
0.3%
O 60
 
0.2%
2 17
 
0.1%
Other values (2) 16
 
0.1%

connection_count
Real number (ℝ)

High correlation 

Distinct361
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.048019
Minimum1
Maximum511
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size109.8 KiB
2025-04-18T10:53:06.826653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median5
Q315
95-th percentile181
Maximum511
Range510
Interquartile range (IQR)14

Descriptive statistics

Standard deviation63.798968
Coefficient of variation (CV)2.2746336
Kurtosis13.888828
Mean28.048019
Median Absolute Deviation (MAD)4
Skewness3.4706852
Sum393682
Variance4070.3083
MonotonicityNot monotonic
2025-04-18T10:53:06.891026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 4183
29.8%
2 1483
 
10.6%
3 706
 
5.0%
4 622
 
4.4%
5 545
 
3.9%
7 445
 
3.2%
6 443
 
3.2%
8 369
 
2.6%
9 314
 
2.2%
10 287
 
2.0%
Other values (351) 4639
33.1%
ValueCountFrequency (%)
1 4183
29.8%
2 1483
 
10.6%
3 706
 
5.0%
4 622
 
4.4%
5 545
 
3.9%
6 443
 
3.2%
7 445
 
3.2%
8 369
 
2.6%
9 314
 
2.2%
10 287
 
2.0%
ValueCountFrequency (%)
511 21
0.1%
510 1
 
< 0.1%
509 1
 
< 0.1%
500 2
 
< 0.1%
497 1
 
< 0.1%
496 1
 
< 0.1%
492 1
 
< 0.1%
489 1
 
< 0.1%
487 1
 
< 0.1%
484 1
 
< 0.1%

destination_server_different_host_rate
Real number (ℝ)

High correlation  Zeros 

Distinct58
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.025828584
Minimum0
Maximum1
Zeros7285
Zeros (%)51.9%
Negative0
Negative (%)0.0%
Memory size109.8 KiB
2025-04-18T10:53:06.953071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.03
95-th percentile0.1
Maximum1
Range1
Interquartile range (IQR)0.03

Descriptive statistics

Standard deviation0.071389818
Coefficient of variation (CV)2.763985
Kurtosis81.523541
Mean0.025828584
Median Absolute Deviation (MAD)0
Skewness7.726367
Sum362.53
Variance0.0050965061
MonotonicityNot monotonic
2025-04-18T10:53:07.015430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7285
51.9%
0.02 1601
 
11.4%
0.01 1452
 
10.3%
0.03 942
 
6.7%
0.04 844
 
6.0%
0.05 600
 
4.3%
0.06 255
 
1.8%
0.07 202
 
1.4%
0.08 82
 
0.6%
0.09 66
 
0.5%
Other values (48) 707
 
5.0%
ValueCountFrequency (%)
0 7285
51.9%
0.01 1452
 
10.3%
0.02 1601
 
11.4%
0.03 942
 
6.7%
0.04 844
 
6.0%
0.05 600
 
4.3%
0.06 255
 
1.8%
0.07 202
 
1.4%
0.08 82
 
0.6%
0.09 66
 
0.5%
ValueCountFrequency (%)
1 26
0.2%
0.86 1
 
< 0.1%
0.75 3
 
< 0.1%
0.67 11
 
0.1%
0.6 3
 
< 0.1%
0.57 1
 
< 0.1%
0.55 1
 
< 0.1%
0.52 1
 
< 0.1%
0.51 6
 
< 0.1%
0.5 31
0.2%

suspicious_activity
Real number (ℝ)

High correlation  Zeros 

Distinct20
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.22128812
Minimum0
Maximum77
Zeros13783
Zeros (%)98.2%
Negative0
Negative (%)0.0%
Memory size109.8 KiB
2025-04-18T10:53:07.068334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum77
Range77
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.3277677
Coefficient of variation (CV)10.519172
Kurtosis208.53121
Mean0.22128812
Median Absolute Deviation (MAD)0
Skewness13.125878
Sum3106
Variance5.4185023
MonotonicityNot monotonic
2025-04-18T10:53:07.118717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 13783
98.2%
30 55
 
0.4%
1 44
 
0.3%
2 35
 
0.2%
4 33
 
0.2%
6 25
 
0.2%
22 13
 
0.1%
5 12
 
0.1%
24 9
 
0.1%
19 8
 
0.1%
Other values (10) 19
 
0.1%
ValueCountFrequency (%)
0 13783
98.2%
1 44
 
0.3%
2 35
 
0.2%
3 3
 
< 0.1%
4 33
 
0.2%
5 12
 
0.1%
6 25
 
0.2%
7 1
 
< 0.1%
11 1
 
< 0.1%
12 1
 
< 0.1%
ValueCountFrequency (%)
77 1
 
< 0.1%
30 55
0.4%
28 2
 
< 0.1%
25 1
 
< 0.1%
24 9
 
0.1%
22 13
 
0.1%
19 8
 
0.1%
18 5
 
< 0.1%
17 1
 
< 0.1%
14 3
 
< 0.1%

protocol
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size713.2 KiB
tcp
11179 
udp
2534 
icmp
 
323

Length

Max length4
Median length3
Mean length3.0230123
Min length3

Characters and Unicode

Total characters42431
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtcp
2nd rowtcp
3rd rowtcp
4th rowtcp
5th rowtcp

Common Values

ValueCountFrequency (%)
tcp 11179
79.6%
udp 2534
 
18.1%
icmp 323
 
2.3%

Length

2025-04-18T10:53:07.175637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-18T10:53:07.210050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
tcp 11179
79.6%
udp 2534
 
18.1%
icmp 323
 
2.3%

Most occurring characters

ValueCountFrequency (%)
p 14036
33.1%
c 11502
27.1%
t 11179
26.3%
u 2534
 
6.0%
d 2534
 
6.0%
i 323
 
0.8%
m 323
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 42431
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
p 14036
33.1%
c 11502
27.1%
t 11179
26.3%
u 2534
 
6.0%
d 2534
 
6.0%
i 323
 
0.8%
m 323
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 42431
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
p 14036
33.1%
c 11502
27.1%
t 11179
26.3%
u 2534
 
6.0%
d 2534
 
6.0%
i 323
 
0.8%
m 323
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 42431
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
p 14036
33.1%
c 11502
27.1%
t 11179
26.3%
u 2534
 
6.0%
d 2534
 
6.0%
i 323
 
0.8%
m 323
 
0.8%

server_different_host_rate
Real number (ℝ)

Zeros 

Distinct57
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.12214377
Minimum0
Maximum1
Zeros9121
Zeros (%)65.0%
Negative0
Negative (%)0.0%
Memory size109.8 KiB
2025-04-18T10:53:07.258982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.1
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.26655653
Coefficient of variation (CV)2.1823178
Kurtosis5.065251
Mean0.12214377
Median Absolute Deviation (MAD)0
Skewness2.4876787
Sum1714.41
Variance0.071052383
MonotonicityNot monotonic
2025-04-18T10:53:07.322221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9121
65.0%
1 895
 
6.4%
0.01 586
 
4.2%
0.67 193
 
1.4%
0.5 172
 
1.2%
0.12 169
 
1.2%
0.25 162
 
1.2%
0.33 159
 
1.1%
0.02 153
 
1.1%
0.11 138
 
1.0%
Other values (47) 2288
 
16.3%
ValueCountFrequency (%)
0 9121
65.0%
0.01 586
 
4.2%
0.02 153
 
1.1%
0.03 40
 
0.3%
0.04 39
 
0.3%
0.05 59
 
0.4%
0.06 115
 
0.8%
0.07 109
 
0.8%
0.08 124
 
0.9%
0.09 111
 
0.8%
ValueCountFrequency (%)
1 895
6.4%
0.88 1
 
< 0.1%
0.83 1
 
< 0.1%
0.8 14
 
0.1%
0.75 53
 
0.4%
0.71 3
 
< 0.1%
0.67 193
 
1.4%
0.62 3
 
< 0.1%
0.6 39
 
0.3%
0.57 7
 
< 0.1%

destination_server_syn_error_rate
Real number (ℝ)

High correlation  Zeros 

Distinct77
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.032571958
Minimum0
Maximum1
Zeros12549
Zeros (%)89.4%
Negative0
Negative (%)0.0%
Memory size109.8 KiB
2025-04-18T10:53:07.384589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.02
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.16848298
Coefficient of variation (CV)5.1726392
Kurtosis27.304873
Mean0.032571958
Median Absolute Deviation (MAD)0
Skewness5.3695683
Sum457.18
Variance0.028386516
MonotonicityNot monotonic
2025-04-18T10:53:07.449911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12549
89.4%
0.01 726
 
5.2%
1 358
 
2.6%
0.02 122
 
0.9%
0.03 25
 
0.2%
0.08 15
 
0.1%
0.06 13
 
0.1%
0.12 13
 
0.1%
0.07 12
 
0.1%
0.04 12
 
0.1%
Other values (67) 191
 
1.4%
ValueCountFrequency (%)
0 12549
89.4%
0.01 726
 
5.2%
0.02 122
 
0.9%
0.03 25
 
0.2%
0.04 12
 
0.1%
0.05 11
 
0.1%
0.06 13
 
0.1%
0.07 12
 
0.1%
0.08 15
 
0.1%
0.09 9
 
0.1%
ValueCountFrequency (%)
1 358
2.6%
0.98 1
 
< 0.1%
0.97 3
 
< 0.1%
0.96 4
 
< 0.1%
0.95 3
 
< 0.1%
0.94 3
 
< 0.1%
0.93 1
 
< 0.1%
0.92 3
 
< 0.1%
0.91 2
 
< 0.1%
0.9 1
 
< 0.1%

destination_host_server_count
Real number (ℝ)

High correlation 

Distinct256
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean182.67875
Minimum0
Maximum255
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size109.8 KiB
2025-04-18T10:53:07.722014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q193
median254
Q3255
95-th percentile255
Maximum255
Range255
Interquartile range (IQR)162

Descriptive statistics

Standard deviation97.214193
Coefficient of variation (CV)0.53215927
Kurtosis-0.9576603
Mean182.67875
Median Absolute Deviation (MAD)1
Skewness-0.8727688
Sum2564079
Variance9450.5993
MonotonicityNot monotonic
2025-04-18T10:53:07.833107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
255 6912
49.2%
254 438
 
3.1%
1 414
 
2.9%
2 258
 
1.8%
3 140
 
1.0%
4 96
 
0.7%
253 90
 
0.6%
5 86
 
0.6%
251 80
 
0.6%
7 74
 
0.5%
Other values (246) 5448
38.8%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 414
2.9%
2 258
1.8%
3 140
 
1.0%
4 96
 
0.7%
5 86
 
0.6%
6 67
 
0.5%
7 74
 
0.5%
8 72
 
0.5%
9 59
 
0.4%
ValueCountFrequency (%)
255 6912
49.2%
254 438
 
3.1%
253 90
 
0.6%
252 33
 
0.2%
251 80
 
0.6%
250 65
 
0.5%
249 37
 
0.3%
248 40
 
0.3%
247 54
 
0.4%
246 45
 
0.3%

destination_same_server_rate
Real number (ℝ)

High correlation  Zeros 

Distinct101
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.78395483
Minimum0
Maximum1
Zeros286
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size109.8 KiB
2025-04-18T10:53:07.934702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.02
Q10.6475
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.3525

Descriptive statistics

Standard deviation0.34834584
Coefficient of variation (CV)0.44434427
Kurtosis0.063205416
Mean0.78395483
Median Absolute Deviation (MAD)0
Skewness-1.3121883
Sum11003.59
Variance0.12134482
MonotonicityNot monotonic
2025-04-18T10:53:07.997907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 8435
60.1%
0.01 358
 
2.6%
0 286
 
2.0%
0.98 168
 
1.2%
0.02 166
 
1.2%
0.96 155
 
1.1%
0.04 125
 
0.9%
0.95 122
 
0.9%
0.99 121
 
0.9%
0.03 119
 
0.8%
Other values (91) 3981
28.4%
ValueCountFrequency (%)
0 286
2.0%
0.01 358
2.6%
0.02 166
1.2%
0.03 119
 
0.8%
0.04 125
 
0.9%
0.05 115
 
0.8%
0.06 88
 
0.6%
0.07 92
 
0.7%
0.08 67
 
0.5%
0.09 68
 
0.5%
ValueCountFrequency (%)
1 8435
60.1%
0.99 121
 
0.9%
0.98 168
 
1.2%
0.97 98
 
0.7%
0.96 155
 
1.1%
0.95 122
 
0.9%
0.94 82
 
0.6%
0.93 78
 
0.6%
0.92 70
 
0.5%
0.91 65
 
0.5%

fragment_errors
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size685.5 KiB
0
14028 
3
 
7
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14036
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14028
99.9%
3 7
 
< 0.1%
1 1
 
< 0.1%

Length

2025-04-18T10:53:08.058062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-18T10:53:08.097419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 14028
99.9%
3 7
 
< 0.1%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 14028
99.9%
3 7
 
< 0.1%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14036
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 14028
99.9%
3 7
 
< 0.1%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14036
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 14028
99.9%
3 7
 
< 0.1%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14036
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 14028
99.9%
3 7
 
< 0.1%
1 1
 
< 0.1%

compromised_count
Real number (ℝ)

Skewed  Zeros 

Distinct27
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.39334568
Minimum0
Maximum884
Zeros13931
Zeros (%)99.3%
Negative0
Negative (%)0.0%
Memory size109.8 KiB
2025-04-18T10:53:08.134235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum884
Range884
Interquartile range (IQR)0

Descriptive statistics

Standard deviation13.949886
Coefficient of variation (CV)35.464698
Kurtosis2404.8145
Mean0.39334568
Median Absolute Deviation (MAD)0
Skewness46.451811
Sum5521
Variance194.59931
MonotonicityNot monotonic
2025-04-18T10:53:08.188928image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 13931
99.3%
1 33
 
0.2%
2 20
 
0.1%
4 10
 
0.1%
3 7
 
< 0.1%
6 7
 
< 0.1%
5 5
 
< 0.1%
7 2
 
< 0.1%
12 2
 
< 0.1%
151 2
 
< 0.1%
Other values (17) 17
 
0.1%
ValueCountFrequency (%)
0 13931
99.3%
1 33
 
0.2%
2 20
 
0.1%
3 7
 
< 0.1%
4 10
 
0.1%
5 5
 
< 0.1%
6 7
 
< 0.1%
7 2
 
< 0.1%
9 1
 
< 0.1%
12 2
 
< 0.1%
ValueCountFrequency (%)
884 1
< 0.1%
789 1
< 0.1%
558 1
< 0.1%
520 1
< 0.1%
462 1
< 0.1%
457 1
< 0.1%
371 1
< 0.1%
217 1
< 0.1%
193 1
< 0.1%
157 1
< 0.1%

source_bytes
Real number (ℝ)

Skewed  Zeros 

Distinct1626
Distinct (%)11.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11341.808
Minimum0
Maximum7665876
Zeros1134
Zeros (%)8.1%
Negative0
Negative (%)0.0%
Memory size109.8 KiB
2025-04-18T10:53:08.246734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q178
median229
Q3323
95-th percentile2225
Maximum7665876
Range7665876
Interquartile range (IQR)245

Descriptive statistics

Standard deviation169682.17
Coefficient of variation (CV)14.960769
Kurtosis739.49805
Mean11341.808
Median Absolute Deviation (MAD)103
Skewness23.366042
Sum1.5919362 × 108
Variance2.8792038 × 1010
MonotonicityNot monotonic
2025-04-18T10:53:08.314522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1134
 
8.1%
44 464
 
3.3%
45 416
 
3.0%
46 284
 
2.0%
43 231
 
1.6%
147 210
 
1.5%
105 204
 
1.5%
42 156
 
1.1%
30 130
 
0.9%
146 130
 
0.9%
Other values (1616) 10677
76.1%
ValueCountFrequency (%)
0 1134
8.1%
1 24
 
0.2%
5 1
 
< 0.1%
6 16
 
0.1%
7 26
 
0.2%
8 66
 
0.5%
9 39
 
0.3%
10 32
 
0.2%
11 14
 
0.1%
12 45
 
0.3%
ValueCountFrequency (%)
7665876 1
 
< 0.1%
7248552 1
 
< 0.1%
5131424 1
 
< 0.1%
5097472 1
 
< 0.1%
2280318 1
 
< 0.1%
2194620 2
 
< 0.1%
2194619 44
0.3%
2187080 1
 
< 0.1%
1519900 1
 
< 0.1%
1126224 1
 
< 0.1%
Distinct62
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size740.6 KiB
2025-04-18T10:53:08.404275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length11
Median length4
Mean length5.0228698
Min length3

Characters and Unicode

Total characters70501
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st rowhttp
2nd rowlink
3rd rowbgp
4th rowctf
5th rowiso_tsap
ValueCountFrequency (%)
http 7588
54.1%
domain_u 1817
 
12.9%
smtp 1391
 
9.9%
ftp_data 1022
 
7.3%
other 533
 
3.8%
private 405
 
2.9%
telnet 224
 
1.6%
ftp 186
 
1.3%
eco_i 134
 
1.0%
finger 129
 
0.9%
Other values (52) 607
 
4.3%
2025-04-18T10:53:08.587991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 20400
28.9%
p 10909
15.5%
h 8217
11.7%
a 4387
 
6.2%
m 3287
 
4.7%
_ 3278
 
4.6%
d 2886
 
4.1%
i 2778
 
3.9%
o 2596
 
3.7%
n 2287
 
3.2%
Other values (28) 9476
13.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 70501
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 20400
28.9%
p 10909
15.5%
h 8217
11.7%
a 4387
 
6.2%
m 3287
 
4.7%
_ 3278
 
4.6%
d 2886
 
4.1%
i 2778
 
3.9%
o 2596
 
3.7%
n 2287
 
3.2%
Other values (28) 9476
13.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 70501
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 20400
28.9%
p 10909
15.5%
h 8217
11.7%
a 4387
 
6.2%
m 3287
 
4.7%
_ 3278
 
4.6%
d 2886
 
4.1%
i 2778
 
3.9%
o 2596
 
3.7%
n 2287
 
3.2%
Other values (28) 9476
13.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 70501
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 20400
28.9%
p 10909
15.5%
h 8217
11.7%
a 4387
 
6.2%
m 3287
 
4.7%
_ 3278
 
4.6%
d 2886
 
4.1%
i 2778
 
3.9%
o 2596
 
3.7%
n 2287
 
3.2%
Other values (28) 9476
13.4%

destination_syn_error_rate
Real number (ℝ)

High correlation  Zeros 

Distinct98
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.039906669
Minimum0
Maximum1
Zeros12408
Zeros (%)88.4%
Negative0
Negative (%)0.0%
Memory size109.8 KiB
2025-04-18T10:53:08.683314image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.18089257
Coefficient of variation (CV)4.5328908
Kurtosis21.345742
Mean0.039906669
Median Absolute Deviation (MAD)0
Skewness4.7562102
Sum560.13
Variance0.032722122
MonotonicityNot monotonic
2025-04-18T10:53:08.793158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12408
88.4%
0.01 573
 
4.1%
1 360
 
2.6%
0.02 148
 
1.1%
0.03 72
 
0.5%
0.04 38
 
0.3%
0.05 31
 
0.2%
0.25 21
 
0.1%
0.33 20
 
0.1%
0.5 19
 
0.1%
Other values (88) 346
 
2.5%
ValueCountFrequency (%)
0 12408
88.4%
0.01 573
 
4.1%
0.02 148
 
1.1%
0.03 72
 
0.5%
0.04 38
 
0.3%
0.05 31
 
0.2%
0.06 15
 
0.1%
0.07 16
 
0.1%
0.08 14
 
0.1%
0.09 14
 
0.1%
ValueCountFrequency (%)
1 360
2.6%
0.99 11
 
0.1%
0.98 4
 
< 0.1%
0.97 2
 
< 0.1%
0.96 4
 
< 0.1%
0.95 4
 
< 0.1%
0.94 4
 
< 0.1%
0.93 8
 
0.1%
0.92 2
 
< 0.1%
0.91 1
 
< 0.1%

same_server_rate
Real number (ℝ)

High correlation 

Distinct66
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.94019023
Minimum0
Maximum1
Zeros33
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size109.8 KiB
2025-04-18T10:53:08.897331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3
Q11
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.21191794
Coefficient of variation (CV)0.225399
Kurtosis10.595766
Mean0.94019023
Median Absolute Deviation (MAD)0
Skewness-3.4703241
Sum13196.51
Variance0.044909212
MonotonicityNot monotonic
2025-04-18T10:53:08.963977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 12764
90.9%
0.5 236
 
1.7%
0.99 142
 
1.0%
0.33 85
 
0.6%
0.2 68
 
0.5%
0.01 53
 
0.4%
0.25 51
 
0.4%
0.05 48
 
0.3%
0.07 48
 
0.3%
0.04 46
 
0.3%
Other values (56) 495
 
3.5%
ValueCountFrequency (%)
0 33
0.2%
0.01 53
0.4%
0.02 35
0.2%
0.03 33
0.2%
0.04 46
0.3%
0.05 48
0.3%
0.06 33
0.2%
0.07 48
0.3%
0.08 41
0.3%
0.09 23
0.2%
ValueCountFrequency (%)
1 12764
90.9%
0.99 142
 
1.0%
0.98 8
 
0.1%
0.97 5
 
< 0.1%
0.96 3
 
< 0.1%
0.95 1
 
< 0.1%
0.94 1
 
< 0.1%
0.93 1
 
< 0.1%
0.92 2
 
< 0.1%
0.9 1
 
< 0.1%

reset_error_rate
Real number (ℝ)

High correlation  Zeros 

Distinct47
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.048265175
Minimum0
Maximum1
Zeros13294
Zeros (%)94.7%
Negative0
Negative (%)0.0%
Memory size109.8 KiB
2025-04-18T10:53:09.029429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.25
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.21109102
Coefficient of variation (CV)4.3735679
Kurtosis15.883533
Mean0.048265175
Median Absolute Deviation (MAD)0
Skewness4.2133109
Sum677.45
Variance0.04455942
MonotonicityNot monotonic
2025-04-18T10:53:09.096550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0 13294
94.7%
1 618
 
4.4%
0.5 18
 
0.1%
0.33 9
 
0.1%
0.67 5
 
< 0.1%
0.25 5
 
< 0.1%
0.92 4
 
< 0.1%
0.75 4
 
< 0.1%
0.02 4
 
< 0.1%
0.93 4
 
< 0.1%
Other values (37) 71
 
0.5%
ValueCountFrequency (%)
0 13294
94.7%
0.02 4
 
< 0.1%
0.03 1
 
< 0.1%
0.04 3
 
< 0.1%
0.05 3
 
< 0.1%
0.06 1
 
< 0.1%
0.07 4
 
< 0.1%
0.08 3
 
< 0.1%
0.09 1
 
< 0.1%
0.1 3
 
< 0.1%
ValueCountFrequency (%)
1 618
4.4%
0.95 2
 
< 0.1%
0.94 3
 
< 0.1%
0.93 4
 
< 0.1%
0.92 4
 
< 0.1%
0.91 3
 
< 0.1%
0.9 1
 
< 0.1%
0.88 1
 
< 0.1%
0.85 1
 
< 0.1%
0.84 1
 
< 0.1%

server_request_count
Real number (ℝ)

High correlation 

Distinct345
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.400328
Minimum1
Maximum511
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size109.8 KiB
2025-04-18T10:53:09.160543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median6
Q318
95-th percentile168
Maximum511
Range510
Interquartile range (IQR)16

Descriptive statistics

Standard deviation60.297916
Coefficient of variation (CV)2.2006275
Kurtosis12.951508
Mean27.400328
Median Absolute Deviation (MAD)5
Skewness3.3989575
Sum384591
Variance3635.8387
MonotonicityNot monotonic
2025-04-18T10:53:09.226484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 3441
24.5%
2 1654
 
11.8%
3 732
 
5.2%
4 597
 
4.3%
5 472
 
3.4%
6 425
 
3.0%
7 419
 
3.0%
8 381
 
2.7%
9 331
 
2.4%
11 309
 
2.2%
Other values (335) 5275
37.6%
ValueCountFrequency (%)
1 3441
24.5%
2 1654
11.8%
3 732
 
5.2%
4 597
 
4.3%
5 472
 
3.4%
6 425
 
3.0%
7 419
 
3.0%
8 381
 
2.7%
9 331
 
2.4%
10 297
 
2.1%
ValueCountFrequency (%)
511 11
0.1%
510 1
 
< 0.1%
497 1
 
< 0.1%
496 1
 
< 0.1%
489 1
 
< 0.1%
487 1
 
< 0.1%
484 1
 
< 0.1%
467 1
 
< 0.1%
456 1
 
< 0.1%
442 1
 
< 0.1%

destination_server_reset_error_rate
Real number (ℝ)

High correlation  Zeros 

Distinct99
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.048468225
Minimum0
Maximum1
Zeros12691
Zeros (%)90.4%
Negative0
Negative (%)0.0%
Memory size109.8 KiB
2025-04-18T10:53:09.291122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.4825
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.20033984
Coefficient of variation (CV)4.1334264
Kurtosis15.957109
Mean0.048468225
Median Absolute Deviation (MAD)0
Skewness4.1795863
Sum680.3
Variance0.04013605
MonotonicityNot monotonic
2025-04-18T10:53:09.358167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12691
90.4%
1 337
 
2.4%
0.01 218
 
1.6%
0.02 94
 
0.7%
0.03 63
 
0.4%
0.04 43
 
0.3%
0.99 33
 
0.2%
0.05 33
 
0.2%
0.98 30
 
0.2%
0.97 19
 
0.1%
Other values (89) 475
 
3.4%
ValueCountFrequency (%)
0 12691
90.4%
0.01 218
 
1.6%
0.02 94
 
0.7%
0.03 63
 
0.4%
0.04 43
 
0.3%
0.05 33
 
0.2%
0.06 16
 
0.1%
0.07 15
 
0.1%
0.08 16
 
0.1%
0.09 10
 
0.1%
ValueCountFrequency (%)
1 337
2.4%
0.99 33
 
0.2%
0.98 30
 
0.2%
0.97 19
 
0.1%
0.96 15
 
0.1%
0.95 9
 
0.1%
0.94 10
 
0.1%
0.93 8
 
0.1%
0.92 6
 
< 0.1%
0.91 7
 
< 0.1%

server_reset_error_rate
Real number (ℝ)

High correlation  Zeros 

Distinct40
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.049172129
Minimum0
Maximum1
Zeros13215
Zeros (%)94.2%
Negative0
Negative (%)0.0%
Memory size109.8 KiB
2025-04-18T10:53:09.420677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.33
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.21187079
Coefficient of variation (CV)4.3087578
Kurtosis15.690672
Mean0.049172129
Median Absolute Deviation (MAD)0
Skewness4.1860566
Sum690.18
Variance0.044889233
MonotonicityNot monotonic
2025-04-18T10:53:09.482552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0 13215
94.2%
1 635
 
4.5%
0.5 24
 
0.2%
0.33 13
 
0.1%
0.04 12
 
0.1%
0.06 10
 
0.1%
0.17 9
 
0.1%
0.25 9
 
0.1%
0.02 9
 
0.1%
0.14 8
 
0.1%
Other values (30) 92
 
0.7%
ValueCountFrequency (%)
0 13215
94.2%
0.02 9
 
0.1%
0.03 8
 
0.1%
0.04 12
 
0.1%
0.05 6
 
< 0.1%
0.06 10
 
0.1%
0.07 6
 
< 0.1%
0.08 8
 
0.1%
0.09 2
 
< 0.1%
0.1 6
 
< 0.1%
ValueCountFrequency (%)
1 635
4.5%
0.85 1
 
< 0.1%
0.84 1
 
< 0.1%
0.83 3
 
< 0.1%
0.81 2
 
< 0.1%
0.8 3
 
< 0.1%
0.79 2
 
< 0.1%
0.76 1
 
< 0.1%
0.75 4
 
< 0.1%
0.74 1
 
< 0.1%

server_syn_error_rate
Real number (ℝ)

High correlation  Zeros 

Distinct37
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.037901111
Minimum0
Maximum1
Zeros13194
Zeros (%)94.0%
Negative0
Negative (%)0.0%
Memory size109.8 KiB
2025-04-18T10:53:09.541718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.11
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.17924339
Coefficient of variation (CV)4.7292383
Kurtosis23.122098
Mean0.037901111
Median Absolute Deviation (MAD)0
Skewness4.9374306
Sum531.98
Variance0.032128191
MonotonicityNot monotonic
2025-04-18T10:53:09.602633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 13194
94.0%
1 430
 
3.1%
0.5 77
 
0.5%
0.33 50
 
0.4%
0.25 41
 
0.3%
0.2 31
 
0.2%
0.05 26
 
0.2%
0.04 20
 
0.1%
0.17 20
 
0.1%
0.03 20
 
0.1%
Other values (27) 127
 
0.9%
ValueCountFrequency (%)
0 13194
94.0%
0.01 1
 
< 0.1%
0.02 13
 
0.1%
0.03 20
 
0.1%
0.04 20
 
0.1%
0.05 26
 
0.2%
0.06 10
 
0.1%
0.07 16
 
0.1%
0.08 10
 
0.1%
0.09 10
 
0.1%
ValueCountFrequency (%)
1 430
3.1%
0.95 4
 
< 0.1%
0.91 1
 
< 0.1%
0.89 1
 
< 0.1%
0.88 1
 
< 0.1%
0.78 1
 
< 0.1%
0.75 1
 
< 0.1%
0.67 4
 
< 0.1%
0.57 1
 
< 0.1%
0.53 1
 
< 0.1%

destination_bytes
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct3886
Distinct (%)27.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4226.1101
Minimum0
Maximum5131424
Zeros2728
Zeros (%)19.4%
Negative0
Negative (%)0.0%
Memory size109.8 KiB
2025-04-18T10:53:09.678431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q172
median346.5
Q31924.25
95-th percentile12609
Maximum5131424
Range5131424
Interquartile range (IQR)1852.25

Descriptive statistics

Standard deviation68334.655
Coefficient of variation (CV)16.169635
Kurtosis4583.824
Mean4226.1101
Median Absolute Deviation (MAD)346.5
Skewness63.618407
Sum59317681
Variance4.6696251 × 109
MonotonicityNot monotonic
2025-04-18T10:53:09.793740image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2728
 
19.4%
105 309
 
2.2%
44 115
 
0.8%
330 105
 
0.7%
42 105
 
0.7%
332 103
 
0.7%
331 97
 
0.7%
329 88
 
0.6%
4 88
 
0.6%
328 86
 
0.6%
Other values (3876) 10212
72.8%
ValueCountFrequency (%)
0 2728
19.4%
1 1
 
< 0.1%
4 88
 
0.6%
15 4
 
< 0.1%
17 7
 
< 0.1%
18 3
 
< 0.1%
24 2
 
< 0.1%
29 8
 
0.1%
30 2
 
< 0.1%
31 3
 
< 0.1%
ValueCountFrequency (%)
5131424 1
< 0.1%
5129964 1
< 0.1%
1639484 1
< 0.1%
1593580 1
< 0.1%
1437092 1
< 0.1%
1281000 1
< 0.1%
954639 1
< 0.1%
646196 1
< 0.1%
646195 1
< 0.1%
558164 1
< 0.1%

guest_login
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size685.5 KiB
0
13865 
1
 
171

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14036
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13865
98.8%
1 171
 
1.2%

Length

2025-04-18T10:53:09.920236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-18T10:53:09.966297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 13865
98.8%
1 171
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 13865
98.8%
1 171
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14036
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 13865
98.8%
1 171
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14036
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 13865
98.8%
1 171
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14036
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 13865
98.8%
1 171
 
1.2%

authentication_status
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size685.5 KiB
1
9561 
0
4475 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14036
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 9561
68.1%
0 4475
31.9%

Length

2025-04-18T10:53:10.006959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-18T10:53:10.040079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 9561
68.1%
0 4475
31.9%

Most occurring characters

ValueCountFrequency (%)
1 9561
68.1%
0 4475
31.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14036
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 9561
68.1%
0 4475
31.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14036
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 9561
68.1%
0 4475
31.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14036
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 9561
68.1%
0 4475
31.9%

destination_host_count
Real number (ℝ)

High correlation 

Distinct256
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean151.48625
Minimum0
Maximum255
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size109.8 KiB
2025-04-18T10:53:10.092831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q143
median168
Q3255
95-th percentile255
Maximum255
Range255
Interquartile range (IQR)212

Descriptive statistics

Standard deviation101.76511
Coefficient of variation (CV)0.67177787
Kurtosis-1.6559092
Mean151.48625
Median Absolute Deviation (MAD)87
Skewness-0.23409611
Sum2126261
Variance10356.138
MonotonicityNot monotonic
2025-04-18T10:53:10.159280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
255 5674
40.4%
1 223
 
1.6%
2 218
 
1.6%
3 168
 
1.2%
4 152
 
1.1%
5 140
 
1.0%
6 137
 
1.0%
8 125
 
0.9%
10 106
 
0.8%
12 103
 
0.7%
Other values (246) 6990
49.8%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 223
1.6%
2 218
1.6%
3 168
1.2%
4 152
1.1%
5 140
1.0%
6 137
1.0%
7 99
0.7%
8 125
0.9%
9 97
0.7%
ValueCountFrequency (%)
255 5674
40.4%
254 12
 
0.1%
253 14
 
0.1%
252 9
 
0.1%
251 10
 
0.1%
250 14
 
0.1%
249 10
 
0.1%
248 10
 
0.1%
247 15
 
0.1%
246 17
 
0.1%

destination_reset_error_rate
Real number (ℝ)

High correlation  Zeros 

Distinct98
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.050431034
Minimum0
Maximum1
Zeros12461
Zeros (%)88.8%
Negative0
Negative (%)0.0%
Memory size109.8 KiB
2025-04-18T10:53:10.349672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.46
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.20380937
Coefficient of variation (CV)4.0413483
Kurtosis15.656168
Mean0.050431034
Median Absolute Deviation (MAD)0
Skewness4.1425404
Sum707.85
Variance0.041538261
MonotonicityNot monotonic
2025-04-18T10:53:10.415156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12461
88.8%
1 466
 
3.3%
0.01 271
 
1.9%
0.02 158
 
1.1%
0.03 85
 
0.6%
0.05 46
 
0.3%
0.04 42
 
0.3%
0.08 33
 
0.2%
0.07 22
 
0.2%
0.83 19
 
0.1%
Other values (88) 433
 
3.1%
ValueCountFrequency (%)
0 12461
88.8%
0.01 271
 
1.9%
0.02 158
 
1.1%
0.03 85
 
0.6%
0.04 42
 
0.3%
0.05 46
 
0.3%
0.06 8
 
0.1%
0.07 22
 
0.2%
0.08 33
 
0.2%
0.09 14
 
0.1%
ValueCountFrequency (%)
1 466
3.3%
0.98 3
 
< 0.1%
0.97 2
 
< 0.1%
0.96 6
 
< 0.1%
0.95 5
 
< 0.1%
0.94 5
 
< 0.1%
0.93 5
 
< 0.1%
0.92 9
 
0.1%
0.91 5
 
< 0.1%
0.9 8
 
0.1%

different_server_rate
Real number (ℝ)

High correlation  Zeros 

Distinct57
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.032923197
Minimum0
Maximum1
Zeros12652
Zeros (%)90.1%
Negative0
Negative (%)0.0%
Memory size109.8 KiB
2025-04-18T10:53:10.478637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.08
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.15193195
Coefficient of variation (CV)4.6147386
Kurtosis28.079684
Mean0.032923197
Median Absolute Deviation (MAD)0
Skewness5.2909111
Sum462.11
Variance0.023083318
MonotonicityNot monotonic
2025-04-18T10:53:10.542957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12652
90.1%
1 230
 
1.6%
0.06 219
 
1.6%
0.01 190
 
1.4%
0.07 105
 
0.7%
0.67 88
 
0.6%
0.5 84
 
0.6%
0.05 73
 
0.5%
0.6 49
 
0.3%
0.02 41
 
0.3%
Other values (47) 305
 
2.2%
ValueCountFrequency (%)
0 12652
90.1%
0.01 190
 
1.4%
0.02 41
 
0.3%
0.03 37
 
0.3%
0.04 16
 
0.1%
0.05 73
 
0.5%
0.06 219
 
1.6%
0.07 105
 
0.7%
0.08 19
 
0.1%
0.09 14
 
0.1%
ValueCountFrequency (%)
1 230
1.6%
0.96 1
 
< 0.1%
0.83 1
 
< 0.1%
0.8 1
 
< 0.1%
0.78 2
 
< 0.1%
0.77 1
 
< 0.1%
0.76 1
 
< 0.1%
0.75 11
 
0.1%
0.69 1
 
< 0.1%
0.67 88
 
0.6%

class
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size754.6 KiB
normal
13449 
anomaly
 
587

Length

Max length7
Median length6
Mean length6.041821
Min length6

Characters and Unicode

Total characters84803
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowanomaly
2nd rowanomaly
3rd rowanomaly
4th rowanomaly
5th rowanomaly

Common Values

ValueCountFrequency (%)
normal 13449
95.8%
anomaly 587
 
4.2%

Length

2025-04-18T10:53:10.601416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-18T10:53:10.633882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
normal 13449
95.8%
anomaly 587
 
4.2%

Most occurring characters

ValueCountFrequency (%)
a 14623
17.2%
o 14036
16.6%
n 14036
16.6%
l 14036
16.6%
m 14036
16.6%
r 13449
15.9%
y 587
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 84803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 14623
17.2%
o 14036
16.6%
n 14036
16.6%
l 14036
16.6%
m 14036
16.6%
r 13449
15.9%
y 587
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 84803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 14623
17.2%
o 14036
16.6%
n 14036
16.6%
l 14036
16.6%
m 14036
16.6%
r 13449
15.9%
y 587
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 84803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 14623
17.2%
o 14036
16.6%
n 14036
16.6%
l 14036
16.6%
m 14036
16.6%
r 13449
15.9%
y 587
 
0.7%

Interactions

2025-04-18T10:53:04.350284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:29.293212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:31.097422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:32.408445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:33.982558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:35.842671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:37.806676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:39.289749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:40.927056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:42.311824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:43.854854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:45.295968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:47.129517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:48.475290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:50.191393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:51.777499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:53.471864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:54.861958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:56.312438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:57.804031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:59.083566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:53:00.356783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:53:01.764791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:53:03.068059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:53:04.401794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:29.349437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:31.148951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:32.462072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:34.069761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:35.934793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:37.882267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:39.343454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:40.977942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:42.362393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:43.904760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:45.346498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:47.185316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:48.667511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:50.247965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:51.861605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:53.523581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-04-18T10:52:56.365163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:57.854540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:59.134552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:53:00.408098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:53:01.815762image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:53:03.120828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:53:04.454692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:29.425393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:31.198806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:32.514352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:34.157961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:36.016260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:37.974483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:39.397113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:41.031007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:42.414066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:43.957143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-04-18T10:52:47.234550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:48.723951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:50.315399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:51.942609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:53.580956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:54.966731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:56.422464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:57.907770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:59.190211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:53:00.460535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:53:01.868006image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:53:03.173548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:53:04.509020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:29.482157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:31.252491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:32.569441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:34.245096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:36.105284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:38.061751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:39.451195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:41.084529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:42.467037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:44.009180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:45.462246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:47.288789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:48.778705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:50.382817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:52.019354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:53.635185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:55.024077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:56.476485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:57.959241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:59.242836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:53:00.512943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:53:01.922552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:53:03.226345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:53:04.560919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:29.540216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:31.305440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:32.622013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:34.331286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:36.192567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:38.152319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:39.630162image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:41.137659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:42.517256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:44.061907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:45.556280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:47.340720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:48.836300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:50.454806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-04-18T10:52:56.535347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:58.016965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:59.295829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:53:00.563980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:53:01.974699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:53:03.280491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:53:04.614082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:52:29.595868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-04-18T10:53:00.303963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:53:01.711480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:53:03.014890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-18T10:53:04.296010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-04-18T10:53:10.688825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
authentication_statusclasscompromised_countconnection_countconnection_statusconnection_timedestination_bytesdestination_different_server_ratedestination_host_countdestination_host_server_countdestination_reset_error_ratedestination_same_server_ratedestination_same_source_port_ratedestination_server_different_host_ratedestination_server_reset_error_ratedestination_server_syn_error_ratedestination_syn_error_ratedifferent_server_ratefragment_errorsguest_loginprotocolreset_error_ratesame_server_rateserver_different_host_rateserver_request_countserver_reset_error_rateserver_syn_error_ratesource_bytessuspicious_activitysyn_error_rate
authentication_status1.0000.2860.0000.5510.4160.1130.0160.2140.2820.4010.2840.3870.2750.1950.2760.2320.2230.2150.0330.0750.7390.3070.3330.2200.4840.3020.2190.0000.0550.216
class0.2861.0000.0000.4410.7200.0820.0000.1570.1700.3930.1440.4450.0800.1260.1700.7480.7260.0950.1140.0130.1320.1460.7070.0910.1250.1370.6970.0250.0000.684
compromised_count0.0000.0001.000-0.0870.0130.1840.1140.0770.003-0.1040.087-0.087-0.029-0.0510.1160.1090.116-0.0200.0000.0000.0000.0080.019-0.056-0.0980.006-0.0080.0600.154-0.004
connection_count0.5510.441-0.0871.0000.130-0.357-0.037-0.2250.3980.286-0.1770.235-0.365-0.274-0.1640.011-0.0080.2570.0000.0350.415-0.154-0.242-0.0350.846-0.1490.126-0.217-0.1380.155
connection_status0.4160.7200.0130.1301.0000.1360.0550.0630.0860.1230.3170.1370.0500.1650.3150.3160.2960.0530.0000.0260.1090.3660.2170.0520.0260.3450.3590.0280.0090.409
connection_time0.1130.0820.184-0.3570.1361.000-0.0030.4280.013-0.4010.008-0.4140.105-0.1330.0230.0140.0280.0280.0000.0000.105-0.021-0.030-0.027-0.357-0.027-0.0750.1750.283-0.072
destination_bytes0.0160.0000.114-0.0370.055-0.0031.000-0.511-0.1680.559-0.1830.504-0.2080.183-0.119-0.083-0.111-0.3340.0000.0000.000-0.2690.3300.1150.089-0.240-0.1180.3710.121-0.160
destination_different_server_rate0.2140.1570.077-0.2250.0630.428-0.5111.0000.200-0.8450.037-0.9390.097-0.317-0.0550.1660.2440.3750.0210.0660.201-0.036-0.379-0.163-0.377-0.0430.109-0.0030.0900.159
destination_host_count0.2820.1700.0030.3980.0860.013-0.1680.2001.000-0.067-0.126-0.235-0.695-0.778-0.183-0.0030.0650.2520.0000.0640.261-0.170-0.245-0.0520.279-0.1590.075-0.129-0.0150.093
destination_host_server_count0.4010.393-0.1040.2860.123-0.4010.559-0.845-0.0671.000-0.1250.862-0.2450.244-0.056-0.183-0.240-0.3850.0280.2510.211-0.0880.3960.2110.455-0.073-0.1170.101-0.119-0.171
destination_reset_error_rate0.2840.1440.087-0.1770.3170.008-0.1830.037-0.126-0.1251.000-0.0580.0900.1580.796-0.0030.062-0.0050.0930.0300.0780.680-0.009-0.050-0.2040.646-0.040-0.1630.042-0.024
destination_same_server_rate0.3870.445-0.0870.2350.137-0.4140.504-0.939-0.2350.862-0.0581.000-0.1060.3150.044-0.185-0.273-0.4150.0480.3320.2150.0220.4320.1960.4220.032-0.1350.015-0.104-0.190
destination_same_source_port_rate0.2750.080-0.029-0.3650.0500.105-0.2080.097-0.695-0.2450.090-0.1061.0000.4990.087-0.037-0.067-0.0570.0360.0360.2710.1150.055-0.091-0.3400.096-0.1050.044-0.037-0.093
destination_server_different_host_rate0.1950.126-0.051-0.2740.165-0.1330.183-0.317-0.7780.2440.1580.3150.4991.0000.2050.007-0.067-0.2620.0000.0080.2130.1820.2510.069-0.1570.167-0.0690.092-0.086-0.097
destination_server_reset_error_rate0.2760.1700.116-0.1640.3150.023-0.119-0.055-0.183-0.0560.7960.0440.0870.2051.0000.0370.014-0.0120.0000.0650.0860.714-0.002-0.045-0.1850.688-0.031-0.1890.054-0.024
destination_server_syn_error_rate0.2320.7480.1090.0110.3160.014-0.0830.166-0.003-0.183-0.003-0.185-0.0370.0070.0371.0000.6200.1960.0000.0000.067-0.014-0.233-0.071-0.079-0.0170.509-0.064-0.0140.499
destination_syn_error_rate0.2230.7260.116-0.0080.2960.028-0.1110.2440.065-0.2400.062-0.273-0.067-0.0670.0140.6201.0000.2350.0000.0020.075-0.019-0.274-0.077-0.125-0.0210.553-0.0690.0220.604
different_server_rate0.2150.095-0.0200.2570.0530.028-0.3340.3750.252-0.385-0.005-0.415-0.057-0.262-0.0120.1960.2351.0000.0000.0130.2490.048-0.956-0.148-0.0430.0250.282-0.287-0.0290.361
fragment_errors0.0330.1140.0000.0000.0000.0000.0000.0210.0000.0280.0930.0480.0360.0000.0000.0000.0000.0001.0000.0000.0500.0000.0120.0000.0000.0000.0000.0000.0000.132
guest_login0.0750.0130.0000.0350.0260.0000.0000.0660.0640.2510.0300.3320.0360.0080.0650.0000.0020.0130.0001.0000.0550.0250.0210.0520.0290.0000.0030.0000.7520.013
protocol0.7390.1320.0000.4150.1090.1050.0000.2010.2610.2110.0780.2150.2710.2130.0860.0670.0750.2490.0500.0551.0000.0790.2200.1110.5070.0810.0780.0140.0250.077
reset_error_rate0.3070.1460.008-0.1540.366-0.021-0.269-0.036-0.170-0.0880.6800.0220.1150.1820.714-0.014-0.0190.0480.0000.0250.0791.000-0.060-0.051-0.1950.934-0.038-0.316-0.011-0.025
same_server_rate0.3330.7070.019-0.2420.217-0.0300.330-0.379-0.2450.396-0.0090.4320.0550.251-0.002-0.233-0.274-0.9560.0120.0210.220-0.0601.0000.1510.080-0.040-0.3270.2800.028-0.410
server_different_host_rate0.2200.091-0.056-0.0350.052-0.0270.115-0.163-0.0520.211-0.0500.196-0.0910.069-0.045-0.071-0.077-0.1480.0000.0520.111-0.0510.1511.0000.287-0.008-0.0390.053-0.063-0.084
server_request_count0.4840.125-0.0980.8460.026-0.3570.089-0.3770.2790.455-0.2040.422-0.340-0.157-0.185-0.079-0.125-0.0430.0000.0290.507-0.1950.0800.2871.000-0.1770.025-0.131-0.142-0.016
server_reset_error_rate0.3020.1370.006-0.1490.345-0.027-0.240-0.043-0.159-0.0730.6460.0320.0960.1670.688-0.017-0.0210.0250.0000.0000.0810.934-0.040-0.008-0.1771.000-0.030-0.293-0.003-0.025
server_syn_error_rate0.2190.697-0.0080.1260.359-0.075-0.1180.1090.075-0.117-0.040-0.135-0.105-0.069-0.0310.5090.5530.2820.0000.0030.078-0.038-0.327-0.0390.025-0.0301.000-0.190-0.0050.906
source_bytes0.0000.0250.060-0.2170.0280.1750.371-0.003-0.1290.101-0.1630.0150.0440.092-0.189-0.064-0.069-0.2870.0000.0000.014-0.3160.2800.053-0.131-0.293-0.1901.0000.115-0.191
suspicious_activity0.0550.0000.154-0.1380.0090.2830.1210.090-0.015-0.1190.042-0.104-0.037-0.0860.054-0.0140.022-0.0290.0000.7520.025-0.0110.028-0.063-0.142-0.003-0.0050.1151.000-0.014
syn_error_rate0.2160.684-0.0040.1550.409-0.072-0.1600.1590.093-0.171-0.024-0.190-0.093-0.097-0.0240.4990.6040.3610.1320.0130.077-0.025-0.410-0.084-0.016-0.0250.906-0.191-0.0141.000

Missing values

2025-04-18T10:53:05.846978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-18T10:53:05.989960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

syn_error_rateconnection_timedestination_same_source_port_ratedestination_different_server_rateconnection_statusconnection_countdestination_server_different_host_ratesuspicious_activityprotocolserver_different_host_ratedestination_server_syn_error_ratedestination_host_server_countdestination_same_server_ratefragment_errorscompromised_countsource_bytesservice_typedestination_syn_error_ratesame_server_ratereset_error_rateserver_request_countdestination_server_reset_error_rateserver_reset_error_rateserver_syn_error_ratedestination_bytesguest_loginauthentication_statusdestination_host_countdestination_reset_error_ratedifferent_server_rateclass
00.0000.010.00SF20.02tcp0.000.0871.000154540http0.01.000.0020.010.000.00831401870.010.00anomaly
11.0000.000.07S02420.00tcp0.001.060.02000link1.00.020.0060.000.001.000002550.000.07anomaly
21.0000.000.08S02340.00tcp0.001.0120.05000bgp1.00.070.00160.000.001.000002550.000.06anomaly
31.0000.000.08S01400.00tcp0.001.010.00000ctf1.00.010.0010.000.001.000002550.000.06anomaly
41.0000.000.07S01260.00tcp0.001.0110.04000iso_tsap1.00.090.00110.000.001.000002550.000.06anomaly
50.0000.570.04SF5110.00icmp0.000.01450.57001032ecr_i0.01.000.005110.000.000.000002550.000.00anomaly
60.9500.020.05S0220.00tcp0.221.0280.11000http1.00.360.0590.000.110.890002550.000.23anomaly
71.0000.000.06S02080.00tcp0.001.080.03000private1.00.040.0080.000.001.000002550.000.06anomaly
80.0001.000.00SF10.50icmp1.000.0221.00008eco_i0.01.000.0050.000.000.0000020.000.00anomaly
90.0000.000.06REJ1470.00tcp0.000.0180.07000ldap0.00.121.00181.001.000.000002551.000.05anomaly
syn_error_rateconnection_timedestination_same_source_port_ratedestination_different_server_rateconnection_statusconnection_countdestination_server_different_host_ratesuspicious_activityprotocolserver_different_host_ratedestination_server_syn_error_ratedestination_host_server_countdestination_same_server_ratefragment_errorscompromised_countsource_bytesservice_typedestination_syn_error_ratesame_server_ratereset_error_rateserver_request_countdestination_server_reset_error_rateserver_reset_error_rateserver_syn_error_ratedestination_bytesguest_loginauthentication_statusdestination_host_countdestination_reset_error_ratedifferent_server_rateclass
140260.000.000.00SF150.000tcp0.150.02551.0000193http0.001.00.0200.00.00.002408012550.000.0normal
140270.000.330.00SF30.040tcp0.600.02551.0000252http0.001.00.050.00.00.0061600130.000.0normal
140280.000.010.02SF10.000udp0.000.0840.8700105private0.001.00.010.00.00.0014600970.000.0normal
140290.001.000.00SF10.010udp0.000.02011.000039domain_u0.001.00.010.00.00.009900580.000.0normal
140300.0101.000.00SF10.860tcp0.000.071.0000139telnet0.001.00.010.00.00.0022570110.000.0normal
140310.000.120.07SF10.070tcp0.000.0270.1200748ftp_data0.001.00.010.00.00.00000580.000.0normal
140320.000.000.00SF190.000tcp0.000.02551.0000293http0.001.00.0190.00.00.002486012550.000.0normal
140330.0290.000.02SF10.006tcp0.000.0600.2400329ftp0.001.00.010.10.00.001063112550.030.0normal
140340.010.080.17SF10.000tcp1.000.0110.92002896smtp0.001.00.030.00.00.0033301120.000.0normal
140350.500.010.00S120.010tcp0.060.02551.0000339http0.011.00.0330.00.00.0314600011730.010.0normal